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    "# Soft Actor-Critic (SAC) with TensorFlow\n",
    "\n",
    "In this notebook we will look at Soft Actor-Critic (SAC) algorithm using TensorFlow. It builds on what we saw in `listing8_2` for TD3. However, it is not further improvement of TD3, rather something that came out in parallel of TD3.\n",
    "\n",
    "Click here to access the paper which proposed SAC originally: https://arxiv.org/pdf/1802.09477.pdf\n",
    "\n",
    "SAC forms a bridge between DDPG style deterministic policy gradients and stochastic policy optimization. \n",
    "\n",
    "##### SAC vs TD3\n",
    "\n",
    "Similar:\n",
    "1. Both use Mean Squared Bellman Error(MSBE) minimization towards a common target\n",
    "2. Common target is calculated using target Q networks which are obtained using polyack averaging\n",
    "3. Both use clipped double Q - minimum of two Q values to avoid overestimation\n",
    "\n",
    "Different:\n",
    "1. SAC uses entropy regularization which is absent in TD3\n",
    "2. In TD3 target-policy is used to calculate the next-state-actions while, in SAC we use **current policy** to get actions for next_states\n",
    "3. In TD3 target_policy uses smoothing by adding random noise to actions, However, in SAC the policy learnt is a stochastic one which provides a smoothing effect without any explicit noise addition.\n",
    "\n",
    "#### Q-Loss with Entropy-Regularization\n",
    "\n",
    "Entropy measures randomness in the distribution. Higher the entropy, higher (or flatter) is the distribution. A sharp peaked policy has all its probability centered around that peak and hence it will have low entropy. Entropy is given by:\n",
    "\n",
    "$$H(P) = \\underset{x \\sim P}{E}\\left[{-\\log P(x)}\\right] \\;\\;\\;\\;\\text{where P is distribution of random variable x}$$\n",
    "\n",
    "With entropy regularization, the policy is trained to maximize a trade-off between expected return and entropy with  $\\alpha$ controlling the trade-off. The updated maximization problem is:\n",
    "\n",
    "The policy is trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy.\n",
    "\n",
    "$$\\pi^* = \\arg \\max_{\\pi} \\underset{\\tau \\sim \\pi}{E}\\left[{ \\sum_{t=0}^{\\infty} \\gamma^t \\left( R(s_t, a_t, s_{t+1}) + \\alpha H(\\pi(\\cdot|s_t)) \\right)}\\right]$$\n",
    "\n",
    "In this setting, $V^{\\pi}$ is changed to include the entropy from every time step:\n",
    "\n",
    "$$V^{\\pi}(s) = \\underset{\\tau \\sim \\pi}{E} \\left[{ \\left. \\sum_{t=0}^{\\infty} \\gamma^t \\left( R(s_t, a_t, s_{t+1}) + \\alpha H\\left(\\pi(\\cdot|s_t)\\right) \\right) \\right| s_0 = s} \\right]$$\n",
    "\n",
    "and, $Q^{\\pi}$ is changed to include the entropy bonuses from every time step except the first:\n",
    "\n",
    "$$Q^{\\pi}(s,a) = \\underset{\\tau \\sim \\pi}{E}\\left[{ \\left. \\sum_{t=0}^{\\infty} \\gamma^t  R(s_t, a_t, s_{t+1}) + \\alpha \\sum_{t=1}^{\\infty} \\gamma^t H\\left(\\pi(\\cdot|s_t)\\right)\\right| s_0 = s, a_0 = a}\\right]$$\n",
    "\n",
    "With these definitions, $V^{\\pi}$ and $Q^{\\pi}$ are connected by:\n",
    "\n",
    "$$V^{\\pi}(s) = \\underset{a \\sim \\pi}{E}{Q^{\\pi}(s,a)} + \\alpha H\\left(\\pi(\\cdot|s)\\right)$$\n",
    "\n",
    "and the Bellman equation for $Q^{\\pi}$ is\n",
    "\n",
    "$$Q^{\\pi}(s,a) = \\underset{s' \\sim P \\\\ a' \\sim \\pi}E[\\left[{R(s,a,s') + \\gamma\\left(Q^{\\pi}(s',a') + \\alpha H\\left(\\pi(\\cdot|s')\\right) \\right)} \\right]\\\\\n",
    "= \\underset{s' \\sim P}E\\left[{R(s,a,s') + \\gamma V^{\\pi}(s')}\\right].$$\n",
    "\n",
    "The right hand side is an expectation which we will now convert into a sample estimate.\n",
    "\n",
    "\n",
    "$$Q^{\\pi}(s,a) \\approx r + \\gamma\\left(Q^{\\pi}(s',\\tilde{a}') - \\alpha \\log \\pi(\\tilde{a}'|s') \\right), \\;\\;\\;\\;\\;  \\tilde{a}' \\sim \\pi(\\cdot|s')$$\n",
    "\n",
    "where(s,a,r,s') come form replay buffer and $\\tilde{a}'$ comes from sampling the online/agent policy. In SAC, we do not use target network policy at all. \n",
    "\n",
    "\n",
    "Like TD3, SAC uses clipped Double Q and minimizes Mean Squared Bellman Error (MSBE). Putting it all together, the loss functions for the Q-networks in SAC are:\n",
    "\n",
    "$$L(\\phi_i, {\\mathcal D}) = \\underset{(s,a,r,s',d) \\sim {\\mathcal D}}{{\\mathrm E}}\\left[\n",
    "    \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2\n",
    "    \\right], \\;\\;\\;\\;\\; i=1,2$$\n",
    "    \n",
    "where the target is given by\n",
    "\n",
    "$$y(r, s', d) = r + \\gamma (1 - d) \\left( \\min_{i=1,2} Q_{\\phi_{\\text{targ},i}}(s', \\tilde{a}') - \\alpha \\log \\pi_{\\theta}(\\tilde{a}'|s') \\right), \\;\\;\\;\\;\\; \\tilde{a}' \\sim \\pi_{\\theta}(\\cdot|s')$$\n",
    "\n",
    "\n",
    "We now convert expectations to sample averages:\n",
    "\n",
    "$$L(\\phi_i, {\\mathcal D}) = \\frac{1}{|B|}\\sum_{(s,a,r,s',d) \\in B} \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2, \\;\\;\\;\\;\\; \\text{for } i=1,2$$\n",
    "\n",
    "The final Q Loss we will minimize is:\n",
    "\n",
    "$$Q_{Loss} = L(\\phi_1, {\\mathcal D}) + L(\\phi_1, {\\mathcal D})$$\n",
    "\n",
    "\n",
    "\n",
    "#### Policy Loss with Reparameterization Trick\n",
    "\n",
    "The policy should choose actions to maximize the expected future return and future entropy i.e. V^\\pi\\left(s\\right):\n",
    "\n",
    "$$V^\\pi\\left(s\\right)= \\underset{a\\sim\\pi}{E}[Q^\\pi\\left(s,a\\right)+\\alpha H\\left(\\pi\\left(\\cdot\\middle| s\\right)\\right)]$$\n",
    "\n",
    "We rewrite this as:\n",
    "\n",
    "$$V^\\pi\\left(s\\right)= \\underset{a\\sim\\pi}{E}[Q^\\pi\\left(s,a\\right)\\ -\\ \\alpha\\ log\\ \\pi\\left(a\\middle| s\\right)]$$\n",
    "\n",
    "The Authors of the paper, use reparameterization along with squashed Gaussian policy:\n",
    "\n",
    "$$\\tilde{a_\\theta}\\left(s,\\xi\\right)=\\tanh{\\left(\\mu_\\theta\\left(s\\right)+\\sigma_\\theta\\left(s\\right)\\odot\\xi\\right)},\\;\\;\\;\\;\\;\\xi\\sim\\mathcal{N}\\left(0,I\\right)$$\n",
    "\n",
    "Combining last two equations, and also noting that our policy network is parameterized by θ, the policy network weights, we get:\n",
    "\n",
    "$$\\underset{a \\sim \\pi_{\\theta}}{E}[{Q^{\\pi_{\\theta}}(s,a) - \\alpha \\log \\pi_{\\theta}(a|s)}] = \\underset{\\xi \\sim \\mathcal{N}}{E}[{Q^{\\pi_{\\theta}}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)}]$$\n",
    "\n",
    "\n",
    "Next, we substitute the function approximator for Q taking min of the two Q-functions. \n",
    "\n",
    "$$ Q^{\\pi_{\\theta}}(s,\\tilde{a}_{\\theta}(s,\\xi)) = \\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi))$$\n",
    "\n",
    "The Policy Objective is given by:\n",
    "\n",
    "$$\\max_{\\theta} \\underset{s \\sim \\mathcal{D} \\\\ \\xi \\sim \\mathcal{N}}{E}\\left[ {\\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)} \\right]$$\n",
    "\n",
    "Like before, we use minimizers in PyTorch/TensorFlow. Accordingly, we introduce a -ve sign to convert maximization to a Loss minimization:\n",
    "\n",
    "$$Policy_{Loss} = -\\underset{s \\sim \\mathcal{D} \\\\ \\xi \\sim \\mathcal{N}}{E}\\left[ {\\min_{i=1,2} Q_{\\phi_i}(s,\\tilde{a}_{\\theta}(s,\\xi)) - \\alpha \\log \\pi_{\\theta}(\\tilde{a}_{\\theta}(s,\\xi)|s)} \\right]$$\n",
    "\n",
    "Next we convert the expectation to estimate using samples to get:\n",
    "\n",
    "$$Policy_{Loss} = - \\frac{1}{|B|}\\sum_{s \\in B} \\left(\\min_{i=1,2} Q_{\\phi_i}(s, \\tilde{a}_{\\theta}(s)) - \\alpha \\log \\pi_{\\theta} \\left(\\left. \\tilde{a}_{\\theta}(s) \\right| s\\right) \\right)$$\n",
    "\n",
    "\n",
    "You can read more about Reparameterization and Stochastic Graphs in [Gradient Estimation Using\n",
    "Stochastic Computation Graphs](https://arxiv.org/pdf/1506.05254.pdf)\n",
    "\n",
    "\n",
    "\n",
    "***\n",
    "**Soft Actor Critic (SAC)**\n",
    "***\n",
    " \n",
    "1. Input initial policy parameters $\\theta$,  Q-function parameters $\\phi_1$ and $\\phi_2$, empty replay buffer D\n",
    "\n",
    "2. Set target parameters equal to online parameters $\\phi_{targ,1} \\leftarrow \\phi_1$ and $\\phi_{targ,2} \\leftarrow \\phi_2$\n",
    "\n",
    "3. **repeat**\n",
    "\n",
    "4. Observe state s and select action $a \\sim \\pi_{\\theta}(\\cdot|s)$\n",
    "\n",
    "5. Execute a in environment and observe next state s', reward r, and done signal d\n",
    "\n",
    "6. Store `(s,a,r,s',d)` in Replay Buffer D\n",
    "\n",
    "7. if `s'` is terminal state, reset the environment\n",
    "\n",
    "8. if it's time to update **then**:\n",
    "\n",
    "9. &emsp;&emsp;for j in range (as many updates as required):\n",
    "\n",
    "10. &emsp;&emsp;&emsp;&emsp;Sample a batch B={`(s,a,r,s',d)`} from replay Buffer D:\n",
    "\n",
    "11. &emsp;&emsp;&emsp;&emsp;Compute target for Q functions: \n",
    "\n",
    "$$y (r,s',d) = r + \\gamma (1-d) \\left(\\min_{i=1,2} Q_{\\phi_{\\text{targ}, i}} (s', \\tilde{a}') - \\alpha \\log \\pi_{\\theta}(\\tilde{a}'|s')\\right), \\;\\;\\; \\tilde{a}' \\sim \\pi_{\\theta}(\\cdot|s')$$\n",
    "\n",
    "12. &emsp;&emsp;&emsp;&emsp;Update Q function with one step gradient descent on $\\phi$:\n",
    "$$\\nabla_{\\phi_i} \\frac{1}{|B|}\\sum_{(s,a,r,s',d) \\in B} \\left( Q_{\\phi_i}(s,a) - y(r,s',d) \\right)^2, \\;\\;\\;\\;\\; \\text{for } i=1,2$$\n",
    "\n",
    "13. &emsp;&emsp;&emsp;&emsp;Update policy by one step of gradient ascent using:\n",
    "$$\\nabla_{\\theta} \\frac{1}{|B|}\\sum_{s \\in B} \\left(\\min_{i=1,2} Q_{\\phi_i}(s, \\tilde{a}_{\\theta}(s)) - \\alpha \\log \\pi_{\\theta} \\left(\\left. \\tilde{a}_{\\theta}(s) \\right| s\\right) \\right)$$\n",
    "                      \n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;where $\\tilde{a}_{\\theta}(s)$ is a sample from $\\pi_{\\theta}(\\cdot|s)$ which is differentiable wrt $\\theta$ via the reparameterization trick.\n",
    "\n",
    "14. &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;Update target networks using polyak averaging: \n",
    "$$\\phi_{targ,i} \\leftarrow \\rho\\phi_{targ,i} + (1-\\rho)\\phi_i, \\;\\;\\;\\;\\;  \\text{for } i=1,2$$ \n",
    "***\n",
    "\n",
    "\n",
    "#### Key Features of SAC\n",
    "\n",
    "1. SAC is an off-policy algorithm.\n",
    "2. SAC as implemented can only be used for environments with continuous action spaces. To apply SAC for discrete action space one needs to use Gumbel-Softmax distribution, You can find more about it online.\n",
    "3. SAC learns stochastic policies and is seen as extension of Actor Critic family. \n",
    "\n",
    "\n",
    "\n",
    "#### Our Implementation\n",
    "This notebook follows the code found in OpenAI's [Spinning Up Library](https://spinningup.openai.com/en/latest/algorithms/sac.html). In this notebook we have broken the code into separate code cells with required explanations. Also some of the implementations like ReplayBuffer have been borrowed from our past notebooks to provide continuity. Further some codes like building of networks have been simplified resulting in easier to understand but more verbose code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.signal\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers \n",
    "from tensorflow.keras import Model, Input\n",
    "\n",
    "from copy import deepcopy\n",
    "import gym\n",
    "\n",
    "\n",
    "import os\n",
    "import io\n",
    "import base64\n",
    "import time\n",
    "import glob\n",
    "from IPython.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Environment - Pendulum\n",
    "\n",
    "We can use the setup here to run on any environment which has state as a single vector and actions are continuous. We will build it on `Pendulum-v0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_env(env_name, seed=None):\n",
    "    # remove time limit wrapper from environment\n",
    "    env = gym.make(env_name).unwrapped\n",
    "    if seed is not None:\n",
    "        env.seed(seed)\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State shape: (3,)\n",
      "Action shape: (1,)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWrElEQVR4nO3dfZBU9Z3v8fe353kcUJBhGBmQQSYiuj6OrFbWNfGiIcplSDQJVPYypqiQVCB3TVLZBeVer3uFVct4k0W3jHfjgom7hiSKrFFcFjAuEYPD0y2ewQzPD4PIxAEGmOn+3j/miC3MwMB0Tzf8Pq+qrnP6d56+B6Y/fc7vdJ82d0dEwhXLdAEiklkKAZHAKQREAqcQEAmcQkAkcAoBkcClLQTMbKSZbTSzLWY2JV3bEZGusXR8TsDMcoBNwJ3ATuA9YJy7r0v5xkSkS9J1JDAc2OLuf3T348BLQE2atiUiXZCbpvX2B3YkPd8J/HlHM/fp08cHDRqUplJEBGD58uUfuHvpye3pCgFrp+1T5x1mNhGYCDBw4EDq6urSVIqIAJjZtvba03U6sBMYkPS8AtidPIO7P+fu1e5eXVp6SjiJSDdJVwi8B1SZWaWZ5QNjgXlp2paIdEFaTgfcvdXMJgNvAjnA8+6+Nh3bEpGuSVefAO7+OvB6utYvIqmhTwyKBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgFAIigVMIiAROISASOIWASOAUAiKBUwiIBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgFAIigVMIiAROISASOIWASOAUAiKBUwiIBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgFAIigVMIiAROISASOIWASODOGAJm9ryZNZjZmqS23ma2wMw2R8NeSdOmmtkWM9toZl9IV+EikhqdORKYBYw8qW0KsNDdq4CF0XPMbBgwFrg6WuYfzSwnZdWKSMqdMQTc/W3gw5Oaa4DZ0fhsYExS+0vufszd64EtwPDUlCoi6XCufQJl7r4HIBr2jdr7AzuS5tsZtZ3CzCaaWZ2Z1e3fv/8cyxCRrkp1x6C10+btzejuz7l7tbtXl5aWprgMEemscw2BfWZWDhANG6L2ncCApPkqgN3nXp6IpNu5hsA8oDYarwVeTWofa2YFZlYJVAHLulaiiKRT7plmMLN/BT4H9DGzncDDwGPAHDObAGwHvgLg7mvNbA6wDmgFJrl7PE21i0gKnDEE3H1cB5P+SwfzTwemd6UoEek++sSgSOAUAiKBUwiIBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgFAIigVMIiAROISASOIWASOAUAiKBUwiIBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgFAIigVMIiAROISASOIWASOAUAiKBUwiIBE4hIBI4hYBI4BQCIoFTCIgETiEgEjiFgEjgcjNdgJyf3J3W1lYAYrEYsVjb+4mZZbIsOQdnDAEzGwC8APQDEsBz7v4TM+sN/BIYBGwFvuruB6NlpgITgDjw3939zbRULxlTX1/PfffdR3FxMYMGDaKqqoq+fftyyy23UFlZSc+ePU8Eg2S3zhwJtAI/cPcVZtYDWG5mC4D7gYXu/piZTQGmAH9rZsOAscDVwGXAf5jZZ9w9np5dkExwd44ePUpTUxN79+5lwYIFHDx4kOLiYsrKyhg1ahRjxozhxhtvpLi4WEcIWczc/ewWMHsVeDp6fM7d95hZOfCWu18ZHQXg7n8fzf8m8L/cfWlH66yurva6urpz3QfJgEQiwfHjx0+M79q1i82bN7Nx40bmzZvH6tWrOXr0KLfeeisTJ05k1KhRCoMMM7Pl7l59cvtZHa+Z2SDgBuAPQJm77wGIhn2j2foDO5IW2xm1yQUkFotRWFhIYWEhxcXFVFVVcffdd/PAAw+wYMEClixZwrRp09i1axfjx49n9OjRvP322yf6ESR7dDoEzKwE+A3wgLt/dLpZ22k75XDDzCaaWZ2Z1e3fv7+zZUiWMzNyc3MZNmwYU6dO5a233mLGjBls2bKFL33pSzz88MM0NTVlukxJ0qkQMLM82gLgRXd/OWreF50GEA0bovadwICkxSuA3Sev092fc/dqd68uLS091/oli5kZ/fr143vf+x7z58/nzjvv5Mknn6S2tpb6+nrO9lRU0uOMIWBtJ3E/A9a7+1NJk+YBtdF4LfBqUvtYMysws0qgCliWupLlfBOLxRg6dCizZs3ikUceYfHixdx3332sX79eQZAFOnMk8FngvwF3mNmq6HE38Bhwp5ltBu6MnuPua4E5wDpgPjBJVwbEzCgqKuKHP/whL7zwAnv27GHs2LGsW7dOQZBhZ311IB10dSAs7s6bb77J/fffz6BBg3jllVcoLy/PdFkXvJRcHRBJBTPjrrvu4umnn2b9+vVMmzaN5ubmTJcVLIWAZEQsFmPMmDFMnjyZF198kdmzZ5NIJDJdVpAUApIxubm5PPjgg9x2221Mnz6dTZs2qX8gAxQCklHFxcVMnz6d5uZmfvSjHykEMkAhIBllZtx4443U1tby61//mlWrVmW6pOAoBCTjcnNz+fa3v01hYSEzZ84kHtcV5e6kEJCsMHjwYEaPHs1rr73Gtm3bMl1OUBQC3cTd8Xgcb2099RGPB38unJOTQ21tLYcPH2bu3LnB/3t0J91ZKM3cnfiRIxxcsoQP5s8nEX39NlmssJDet99OLC/v1Gn5+fSsrsZyctpdf6ygoN3lPt724cOHKSkp6dpOdJPrr7+eq666ikWLFvGd73yHwsLCTJcUBIVAmh3dvp36J5+keft2OM118CObNrU/wYzcnj2hg+/hl1x9NQVlZe1OK7r2Wv7vG28wafx48k4Kilh+Pnm9e2fV9/uLiooYMWIEs2bN4sMPP+Syyy7LdElBUAikUaKlhV0//znNW7ee+0rcaf3Tnzqc3Pj733c4bePPf87/WbaMK//zP7n8oos+NS23Z08uuvLKdpezWIzet9/eFj6nTDSKKiuJ5eeDWcpD5Oabb+YnP/kJ7733HjU1NSldt7RPIZBGLQcO0LR6dca2/7tdu9h/+DCLtm+n9oorPvWCbTlwgMZ33ulw2YNLlrQ/wYyigQOx/Hzy+/Shb00NJcOGpSQMzIzrr7+eSy655MRXjbPpSOVCpY7BNDr4zjskjh3LyLYPt7aytKEBBxbt2UNzqi67udO8bRtHNm+mcelS3n/0URqXLk1ZR15FRQU9evRg8+bN6hzsJgqBNOqow647bDt0iI0ftd0A6o9NTaxpbEzJeptaWvi3HTv4tx07ONTSQvzQIbbNnMmhdetSsn6AgoIC1qxZo+8SdBOFQBpdcsstxDLQw+3uLNyzh+PRi+hoIsG/79pFoovvrE0tLfyPlSv5u9Wr+bvVq5m2ciVNURA0vPpqSt65Y7EYl112GQcOHNCRQDdRCKRRXq9eXHzzzd2+3cOtrbzT0HDieY4Zf/jgAw508dTkrb17+X10iuHA7xsaeGvvXgCOf/ABpCgE+vfXfWm7k0IgjSw3l/61tRRXVXXrdjf86U981NLCiPJy8sz42qBBOLD8wIFureNc6TSgeykE0iy/tJQrHnyQ8nHj6HHttZ9c82/vkQIJd7YdOsTD113HVwYNAjNuuvRSfnzzzexrbj5xinAuPt+vH5/t2xej7ZbSf9G3L5/v169tP/v0Sck+JBIJdu7c2eX1SOfpEmGamRn5ffpQPm4cAMf37iV+5Ei78za++27bYXU7Wg4c4NCGDR1uJ3H0KLhjwF39+1OSm8uaxkYM+ODoUf6yrIyKiy7qUuqX5OXxv2+44cQpwOf79aMkL4+ckhL61tSk5HJeIpFg37599M6yDzJdyBQC3eTjP+iC09xLr/iKKzqcljh2jNYO7tfviQSNS5eSOOkWXcf276fHunUczM2loG9fCtpZNn74cIeh1J4eeXn81wGf3FE+p6SEy7/7XUqGDev0Os7k2LFjXHPNNfotw26iEDhPxAoKyC9o72XcpqydT9f1PnaMgf/8zxz7sz/jyn/4B3JyT/3vPrpjB0d37Wp3nYfWrePIli0dbjPVHxYC2LVrF01NTXzmM5/RkUA3UQhcwPLy8igvL2fpsmUcB0rauVx5UVUVF3XQcdn79ttP3+Of4o8NuzsrV66ksbGRwYMHKwS6iY63LmBmxsiRI9m3bx/19fXntLzFYh0/0vAiraur4+KLL6a6+pQ7Y0uaKAQuYGZGdXU18XicJUuWZP2Hb44ePcrChQsZPnw4vXv3znQ5wVAIXOCuu+46hgwZwpw5c7L+tl2rV69m3bp1jBgxgoLT9H9IaikELnBFRUV87WtfY/ny5SxfvjzT5XQoHo/zwgsvUFxczOjRo9Uf0I0UAhc4M+PLX/4yRUVF/PSnP6W1tTXTJbWrvr6euXPnMmrUKAYOHJjpcoKiEAhAZWUl48aNY+7cuaxYsSLr+gZaW1t59tlnaW5uZvLkyeR0cCs1SQ+FQABycnKYNGkSBQUFzJgxI6t+9+/jy4KzZs3i3nvv5YYbbsh0ScFRCARiyJAhTJo0iQULFvCrX/0qa44GmpubmTZtGoWFhfzgBz9QX0AGKAQCYWZMnjyZ6upqHnrooaw4LWhtbWXGjBn87ne/46GHHuLKK69UCGSAQiAgF198MTNnziQnJ4fvf//7Gb1xRyKR4JVXXmHmzJl8/etf5/7779d3BTJE/+oBMTOuueYaHnvsMVasWME3vvEN9u/f3+1BkEgkmD9/PpMnT2bo0KE8+uijFBUVdWsN8gmFQGBisRhf/epXefTRR1m4cCETJkxg9+7d3RYE8Xic3/72t0yYMIHS0lKef/55+kX3JJDMUAgEKCcnh8mTJ/PEE0+wePFi7r33XpYtW5bWIHB3mpubefLJJxk/fjxlZWX88pe/ZFgKv4Eo58jdT/sACoFlwGpgLfBI1N4bWABsjoa9kpaZCmwBNgJfONM2brrpJpfu19ra6i+++KIPGDDA+/bt688884w3NTV5IpFI6Xbi8bhv2LDBx44d6/n5+V5TU+Pvv/9+yrcjpwfUeXuv8fYa/dMhYEBJNJ4H/AG4BXgCmBK1TwEej8aHRYFRAFQC7wM5p9uGQiBz4vG4r1q1ym+77TYvLCz0u+66yxctWuRHjhzp8roTiYTv3bvXn3rqKb/88su9V69ePnXqVP/oo49SULmcrXMOAf90IBQDK4A/j97ly6P2cmCjf3IUMDVpmTeBW0+3XoVAZiUSCT948KA//vjjXlFR4cXFxf7FL37RX375ZW9oaPBEItHpd+1EIuEtLS2+du1anz59ul911VVeUFDgd9xxhy9evNhbWlrSvDfSkY5CwLwT54FmlgMsB4YAz7j735pZo7tfkjTPQXfvZWZPA++6+y+i9p8Bb7j7r09a50RgIsDAgQNv0m/SZ567U19fz7PPPsucOXPYvXs3lZWV3HTTTYwePZoBAwYwdOhQLjrpdw0TiQR79uxh06ZNbNiwgXnz5rF69Wqam5u59dZb+da3vsWoUaMoLi7W+X8Gmdlydz/lRg2dCoGklVwCvAJ8F1jSQQg8Ayw9KQRed/ffdLTe6upqr6ur63Qdkl6JRILdu3fzxhtv8Prrr7N06VIaGxvJzc3l0ksvJT8/HzMjFosRj8dxdw4dOsSHH35IUVERZWVl3HPPPYwZM4bq6mq9+LNERyFwVrcXc/dGM3sLGAnsM7Nyd99jZuXAx792sRMYkLRYBbD73MqWTIjFYlRUVPDNb36T8ePH09jYyLvvvsvWrVvZunUrmzdvZt++fcTjcXr06MHAgQMZMmQIZWVlDB8+nMGDB9OzZ099+Oc8ccYQMLNSoCUKgCJgBPA4MA+oBR6Lhq9Gi8wD/sXMngIuA6pou7og56GCggLKysqoqak5cQ6ZSCROXE606D6DH7/g9Y5//unMkUA5MDvqF4gBc9z9NTNbCswxswnAduArAO6+1szmAOuAVmCSu2f3LW2kU05+wcuF4az6BNJFfQIi6ddRn4AiXSRwCgGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcB1OgTMLMfMVprZa9Hz3ma2wMw2R8NeSfNONbMtZrbRzL6QjsJFJDXO5kjgr4H1Sc+nAAvdvQpYGD3HzIYBY4GrgZHAP5pZTmrKFZFU61QImFkFcA/wT0nNNcDsaHw2MCap/SV3P+bu9cAWYHhKqhWRlOvskcCPgb8BEkltZe6+ByAa9o3a+wM7kubbGbWJSBY6YwiY2Sigwd2Xd3Kd1k6bt7PeiWZWZ2Z1+/fv7+SqRSTVOnMk8FlgtJltBV4C7jCzXwD7zKwcIBo2RPPvBAYkLV8B7D55pe7+nLtXu3t1aWlpF3ZBRLrijCHg7lPdvcLdB9HW4bfI3f8KmAfURrPVAq9G4/OAsWZWYGaVQBWwLOWVi0hK5HZh2ceAOWY2AdgOfAXA3dea2RxgHdAKTHL3eJcrFZG0MPdTTte7XXV1tdfV1WW6DJELmpktd/fqk9v1iUGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwCkERAKnEBAJnEJAJHAKAZHAKQREAqcQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwCkERAJn7p7pGjCz/cBh4INM19JJfTh/aoXzq17Vmj6Xu3vpyY1ZEQIAZlbn7tWZrqMzzqda4fyqV7V2P50OiAROISASuGwKgecyXcBZOJ9qhfOrXtXazbKmT0BEMiObjgREJAMyHgJmNtLMNprZFjObkul6AMzseTNrMLM1SW29zWyBmW2Ohr2Spk2N6t9oZl/o5loHmNliM1tvZmvN7K+ztV4zKzSzZWa2Oqr1kWytNWn7OWa20sxey/Zaz5m7Z+wB5ADvA4OBfGA1MCyTNUV1/SVwI7Amqe0JYEo0PgV4PBofFtVdAFRG+5PTjbWWAzdG4z2ATVFNWVcvYEBJNJ4H/AG4JRtrTar5+8C/AK9l899BVx6ZPhIYDmxx9z+6+3HgJaAmwzXh7m8DH57UXAPMjsZnA2OS2l9y92PuXg9soW2/uoW773H3FdF4E7Ae6J+N9XqbQ9HTvOjh2VgrgJlVAPcA/5TUnJW1dkWmQ6A/sCPp+c6oLRuVufseaHvhAX2j9qzZBzMbBNxA2ztsVtYbHV6vAhqABe6etbUCPwb+BkgktWVrrecs0yFg7bSdb5crsmIfzKwE+A3wgLt/dLpZ22nrtnrdPe7u1wMVwHAzu+Y0s2esVjMbBTS4+/LOLtJO23nxt5zpENgJDEh6XgHszlAtZ7LPzMoBomFD1J7xfTCzPNoC4EV3fzlqztp6Ady9EXgLGEl21vpZYLSZbaXtNPUOM/tFltbaJZkOgfeAKjOrNLN8YCwwL8M1dWQeUBuN1wKvJrWPNbMCM6sEqoBl3VWUmRnwM2C9uz+VzfWaWamZXRKNFwEjgA3ZWKu7T3X3CncfRNvf5SJ3/6tsrLXLMt0zCdxNW4/2+8BDma4nqulfgT1AC20JPwG4FFgIbI6GvZPmfyiqfyPwxW6u9S9oO+z8f8Cq6HF3NtYLXAusjGpdA/zPqD3raj2p7s/xydWBrK71XB76xKBI4DJ9OiAiGaYQEAmcQkAkcAoBkcApBEQCpxAQCZxCQCRwCgGRwP1/5OOwdQIk4TUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = 'Pendulum-v0'\n",
    "\n",
    "env = make_env(env_name)\n",
    "env.reset()\n",
    "plt.imshow(env.render(\"rgb_array\"))\n",
    "state_shape, action_shape = env.observation_space.shape, env.action_space.shape\n",
    "print('State shape: {}'.format(state_shape))\n",
    "print('Action shape: {}'.format(action_shape))\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Policy network (Actor)\n",
    "It is a simple 2 hidden layer network which takes state as input and produces the mean $\\mu$ and $\\log(\\sigma)$ of a normal distribution from which actions are sampled. Calculating $logprob$ requires understanding reparameterization of density distributions, a short explanation is given in Appendix C of the SAC paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_STD_MAX = 2\n",
    "LOG_STD_MIN = -20\n",
    "\n",
    "EPS = 1e-8\n",
    "\n",
    "def gaussian_likelihood(x, mu, log_std):\n",
    "    pre_sum = -0.5 * (((x-mu)/(tf.exp(log_std)+EPS))**2 + 2*log_std + np.log(2*np.pi))\n",
    "    return tf.reduce_sum(pre_sum, axis=1)\n",
    "\n",
    "def apply_squashing_func(mu, pi, logp_pi):\n",
    "    # Adjustment to log prob\n",
    "    # NOTE: This formula is a little bit magic. To get an understanding of where it\n",
    "    # comes from, check out the original SAC paper (arXiv 1801.01290) and look in\n",
    "    # appendix C. This is a more numerically-stable equivalent to Eq 21.\n",
    "    # Try deriving it yourself as a (very difficult) exercise. :)\n",
    "    logp_pi -= tf.reduce_sum(2*(np.log(2) - pi - tf.nn.softplus(-2*pi)), axis=1)\n",
    "\n",
    "    # Squash those unbounded actions!\n",
    "    mu = tf.tanh(mu)\n",
    "    pi = tf.tanh(pi)\n",
    "    return mu, pi, logp_pi\n",
    "\n",
    "class SquashedGaussianMLPActor(tf.keras.Model):\n",
    "    def __init__(self, state_dim, act_dim, act_limit):\n",
    "        super().__init__()\n",
    "        self.act_limit = act_limit\n",
    "        self.fc1 = layers.Dense(256, activation=\"relu\")\n",
    "        self.fc2 = layers.Dense(256, activation=\"relu\")\n",
    "        self.mu_layer = layers.Dense(act_dim)\n",
    "        self.log_std_layer = layers.Dense(act_dim)\n",
    "        self.act_limit = act_limit\n",
    "    \n",
    "    def call(self, s):\n",
    "        x = self.fc1(s)\n",
    "        x = self.fc2(x)\n",
    "        mu = self.mu_layer(x)\n",
    "        log_std = self.log_std_layer(x)\n",
    "        log_std = tf.clip_by_value(log_std, LOG_STD_MIN, LOG_STD_MAX)\n",
    "        std = tf.exp(log_std)\n",
    "        \n",
    "        pi = mu + tf.random.normal(tf.shape(mu)) * std\n",
    "        logp_pi = gaussian_likelihood(pi, mu, log_std)\n",
    "        mu, pi, logp_pi = apply_squashing_func(mu, pi, logp_pi)\n",
    "\n",
    "        mu *= self.act_limit\n",
    "        pi *= self.act_limit\n",
    "\n",
    "        return mu, pi, logp_pi  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Q-network network (Critic)\n",
    "It is a simple 2 hidden layer network which takes state and action as input and produces Q-value as output. We will have two versions of Q-network as Critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPQFunction(tf.keras.Model):\n",
    "    def __init__(self, state_dim, act_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = layers.Dense(256, activation=\"relu\")\n",
    "        self.fc2 = layers.Dense(256, activation=\"relu\")\n",
    "        self.Q = layers.Dense(1)\n",
    "    \n",
    "    def call(self, s, a):\n",
    "        x = tf.concat([s,a], axis=-1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.fc2(x)\n",
    "        q = self.Q(x)\n",
    "        return tf.squeeze(q, -1)    \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Actor and Critic into a single model\n",
    "\n",
    "One policy Network and Two Q-networks as discussed above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLPActorCritic(tf.keras.Model):\n",
    "    def __init__(self, observation_space, action_space):\n",
    "        super().__init__()\n",
    "        self.state_dim = observation_space.shape[0]\n",
    "        self.act_dim = action_space.shape[0]\n",
    "        self.act_limit = action_space.high[0]\n",
    "        \n",
    "        #build Q and policy functions\n",
    "        self.q1 = MLPQFunction(self.state_dim, self.act_dim)\n",
    "        self.q2 = MLPQFunction(self.state_dim, self.act_dim)\n",
    "        self.policy = SquashedGaussianMLPActor(self.state_dim, self.act_dim, self.act_limit)\n",
    "        \n",
    "    def act(self, state, deterministic=False):\n",
    "            mu, pi, _ = self.policy(state)\n",
    "            if deterministic:\n",
    "                return mu.numpy()\n",
    "            else:\n",
    "                return pi.numpy()\n",
    "    \n",
    "    def get_action(self, state, deterministic=False):\n",
    "        return self.act(state.reshape(1,-1).astype(\"float32\")).reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experience replay\n",
    "\n",
    "We will use the replay buffer we saw in chapter 4 listings. Replay buffer is very important in DQN to break the correlation between samples. We use a behavior policy ($\\epsilon-greedy$) to sample from the environment and store the transitions (s,a,r,s',done) into a buffer. These samples are used multiple times in a learning making the process sample efficient. \n",
    "\n",
    "The interface to ReplayBuffer is:\n",
    "* `exp_replay.add(state, action, reward, next_state, done)` - saves (s,a,r,s',done) tuple into the buffer\n",
    "* `exp_replay.sample(batch_size)` - returns states, actions, rewards, next_states and done_flags for `batch_size` random samples.\n",
    "* `len(exp_replay)` - returns number of elements stored in replay buffer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, size=1e6):\n",
    "        self.size = size #max number of items in buffer\n",
    "        self.buffer =[] #array to holde buffer\n",
    "        self.next_id = 0\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.buffer)\n",
    "    \n",
    "    def add(self, state, action, reward, next_state, done):\n",
    "        item = (state, action, reward, next_state, done)\n",
    "        if len(self.buffer) < self.size:\n",
    "            self.buffer.append(item)\n",
    "        else:\n",
    "            self.buffer[self.next_id] = item\n",
    "        self.next_id = (self.next_id + 1) % self.size\n",
    "        \n",
    "    def sample(self, batch_size=32):\n",
    "        idxs = np.random.choice(len(self.buffer), batch_size)\n",
    "        samples = [self.buffer[i] for i in idxs]\n",
    "        states, actions, rewards, next_states, done_flags = list(zip(*samples))\n",
    "        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(done_flags)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q-loss\n",
    "\n",
    "Compute Q-loss as per equations above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def compute_q_loss(agent, target_network, states, actions, rewards, next_states, done_flags,\n",
    "                    gamma, alpha, act_limit, tape):\n",
    "    \n",
    "    # convert numpy array to proper data types\n",
    "    states = states.astype('float32')\n",
    "    actions = actions.astype('float32')\n",
    "    rewards = rewards.astype('float32')\n",
    "    next_states = next_states.astype('float32')\n",
    "    done_flags = done_flags.astype('float32')\n",
    "    \n",
    "    # get q-values for all actions in current states\n",
    "    # use agent network\n",
    "    q1 = agent.q1(states, actions)\n",
    "    q2 = agent.q2(states, actions)\n",
    "    \n",
    "    # Bellman backup for Q function\n",
    "    with tape.stop_recording():\n",
    "        \n",
    "        mu, action_target, action_target_logp = target_network.policy(next_states)\n",
    "        \n",
    "        # Target Q            \n",
    "        q1_target = target_network.q1(next_states, action_target)\n",
    "        q2_target = target_network.q2(next_states, action_target)\n",
    "        q_target = tf.minimum(q1_target, q2_target) \n",
    "        target = rewards + gamma * (1 - done_flags) * (q_target-alpha*action_target_logp)\n",
    "\n",
    "    # MSE loss against Bellman backup\n",
    "    loss_q1 = tf.reduce_mean((q1 - target)**2)\n",
    "    loss_q2 = tf.reduce_mean((q2 - target)**2)\n",
    "    loss_q = loss_q1 + loss_q2\n",
    "    \n",
    "    return loss_q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Policy-Loss\n",
    "\n",
    "Compute Policy Loss as per equations above:\n",
    "    \n",
    "Please note the `-` sign. We need to do gradient ascent but TensorFlow does gradient descent. We convert the ascent to descent using a -ve sign. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_policy_loss(agent, states, alpha, tape):\n",
    "    \n",
    "    # convert numpy array to proper data type\n",
    "    states = states.astype('float32')\n",
    "    \n",
    "    mu, actions, actions_logp = agent.policy(states)\n",
    "    \n",
    "    q1_values = agent.q1(states, actions)\n",
    "    q2_values = agent.q2(states, actions)\n",
    "    q_values = tf.minimum(q1_values, q2_values)\n",
    "    \n",
    "    # Entropy regularised\n",
    "    loss_policy = - tf.reduce_mean(q_values - alpha*actions_logp)\n",
    "    return loss_policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One step gradient Descent on both Policy(Actor) and Q-value(Critic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_step_update(agent, target_network, q_params, q_optimizer, policy_optimizer, \n",
    "                    states, actions, rewards, next_states, done_flags,\n",
    "                    gamma, polyak, alpha, act_limit):\n",
    "    \n",
    "    #one step gradient for q-values\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss_q = compute_q_loss(agent, target_network, states, actions, rewards, next_states, done_flags,\n",
    "                    gamma, alpha, act_limit, tape)\n",
    "    \n",
    "        gradients = tape.gradient(loss_q, q_params)\n",
    "        q_optimizer.apply_gradients(zip(gradients, q_params))\n",
    "    \n",
    "    #Freeze Q-network\n",
    "    agent.q1.trainable=False \n",
    "    agent.q2.trainable=False\n",
    "\n",
    "    #one setep gradient for policy network\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss_policy = compute_policy_loss(agent, states, alpha, tape)\n",
    "        gradients = tape.gradient(loss_policy, agent.policy.trainable_variables)\n",
    "        policy_optimizer.apply_gradients(zip(gradients, agent.policy.trainable_variables))\n",
    "\n",
    "    #UnFreeze Q-network\n",
    "    agent.q1.trainable=True \n",
    "    agent.q2.trainable=True\n",
    "\n",
    "\n",
    "    # update target networks with polyak averaging\n",
    "    updated_model_weights = []\n",
    "    for weights, weights_target in zip(agent.get_weights(), target_network.get_weights()):\n",
    "        new_weights = polyak*weights_target+(1-polyak)*weights\n",
    "        updated_model_weights.append(new_weights)\n",
    "    target_network.set_weights(updated_model_weights)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To Test performance of agent without any stochasticity i.e. perform deterministic actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_agent(env, agent, num_test_episodes, max_ep_len):\n",
    "    ep_rets, ep_lens = [], []\n",
    "    for j in range(num_test_episodes):\n",
    "        state, done, ep_ret, ep_len = env.reset(), False, 0, 0\n",
    "        while not(done or (ep_len == max_ep_len)):\n",
    "            # Take deterministic actions at test time \n",
    "            state, reward, done, _ = env.step(agent.get_action(state, True))\n",
    "            ep_ret += reward\n",
    "            ep_len += 1\n",
    "        ep_rets.append(ep_ret)\n",
    "        ep_lens.append(ep_len)\n",
    "    return np.mean(ep_rets), np.mean(ep_lens)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAC Algorithm\n",
    "\n",
    "We pull all the pieces together to train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sac(env_fn, seed=0, \n",
    "         steps_per_epoch=4000, epochs=5, replay_size=int(1e6), gamma=0.99, \n",
    "         polyak=0.995, policy_lr=1e-3, q_lr=1e-3, alpha=0.2, batch_size=100, start_steps=10000, \n",
    "         update_after=1000, update_every=50, num_test_episodes=10, max_ep_len=1000):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    env, test_env = env_fn(), env_fn()\n",
    "    \n",
    "    ep_rets, ep_lens = [], []\n",
    "\n",
    "    state_dim = env.observation_space.shape\n",
    "    act_dim = env.action_space.shape[0]\n",
    "    \n",
    "    act_limit = env.action_space.high[0]\n",
    "    \n",
    "    agent = MLPActorCritic(env.observation_space, env.action_space)\n",
    "    # force a build of model to initialize the model parameters\n",
    "    s = env.reset()\n",
    "    a = env.action_space.sample()    \n",
    "    agent.policy(np.array([s], dtype=np.float32))\n",
    "    agent.q1(np.array([s],dtype=np.float32),np.array([a],dtype=np.float32))\n",
    "    agent.q2(np.array([s],dtype=np.float32),np.array([a],dtype=np.float32))\n",
    "\n",
    "    # make target network as a deep copy\n",
    "    target_network = deepcopy(agent)\n",
    "    \n",
    "    \n",
    "    # Freeze target networks with respect to optimizers (only update via polyak averaging)\n",
    "    target_network.policy.trainable=False\n",
    "    target_network.q1.trainable = False\n",
    "    target_network.q2.trainable = False\n",
    "    \n",
    "    \n",
    "    # Experience buffer\n",
    "    replay_buffer = ReplayBuffer(replay_size)\n",
    "    \n",
    "    # List of parameters for both Q-networks \n",
    "    q_params = agent.q1.trainable_variables+agent.q2.trainable_variables\n",
    "\n",
    "    #optimizers\n",
    "    q_optimizer = tf.keras.optimizers.Adam(learning_rate=q_lr)\n",
    "    policy_optimizer = tf.keras.optimizers.Adam(learning_rate=policy_lr)\n",
    "    \n",
    "    total_steps = steps_per_epoch*epochs\n",
    "    state, ep_ret, ep_len = env.reset(), 0, 0\n",
    "    \n",
    "    for t in range(total_steps):\n",
    "        if t > start_steps:\n",
    "            action = agent.get_action(state)\n",
    "        else:\n",
    "            action = env.action_space.sample()\n",
    "            \n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        ep_ret += reward\n",
    "        ep_len += 1\n",
    "        \n",
    "        # Ignore the \"done\" signal if it comes from hitting the time\n",
    "        # horizon (that is, when it's an artificial terminal signal\n",
    "        # that isn't based on the agent's state)\n",
    "        done = False if ep_len==max_ep_len else done\n",
    "        \n",
    "        # Store experience to replay buffer\n",
    "        replay_buffer.add(state, action, reward, next_state, done)\n",
    "        \n",
    "        state = next_state\n",
    "        \n",
    "        # End of trajectory handling\n",
    "        if done or (ep_len == max_ep_len):\n",
    "            ep_rets.append(ep_ret)\n",
    "            ep_lens.append(ep_len)\n",
    "            state, ep_ret, ep_len = env.reset(), 0, 0\n",
    "        \n",
    "        # Update handling\n",
    "        if t >= update_after and t % update_every == 0:\n",
    "            for j in range(update_every):\n",
    "                states, actions, rewards, next_states, done_flags = replay_buffer.sample(batch_size)\n",
    "                \n",
    "                one_step_update(\n",
    "                        agent, target_network, q_params, q_optimizer, policy_optimizer, \n",
    "                        states, actions, rewards, next_states, done_flags,\n",
    "                        gamma, polyak, alpha, act_limit\n",
    "                )\n",
    "                        \n",
    "        # End of epoch handling\n",
    "        if (t+1) % steps_per_epoch == 0:\n",
    "            epoch = (t+1) // steps_per_epoch\n",
    "            \n",
    "            avg_ret, avg_len = test_agent(test_env, agent, num_test_episodes, max_ep_len)\n",
    "            print(\"End of epoch: {:.0f}, Training Average Reward: {:.0f}, Training Average Length: {:.0f}\".format(epoch, np.mean(ep_rets), np.mean(ep_lens)))\n",
    "            print(\"End of epoch: {:.0f}, Test Average Reward: {:.0f}, Test Average Length: {:.0f}\".format(epoch, avg_ret, avg_len))\n",
    "            ep_rets, ep_lens = [], []\n",
    "    \n",
    "    return agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of epoch: 1, Training Average Reward: -6224, Training Average Length: 1000\n",
      "End of epoch: 1, Test Average Reward: -196, Test Average Length: 1000\n",
      "End of epoch: 2, Training Average Reward: -5918, Training Average Length: 1000\n",
      "End of epoch: 2, Test Average Reward: -149, Test Average Length: 1000\n",
      "End of epoch: 3, Training Average Reward: -3156, Training Average Length: 1000\n",
      "End of epoch: 3, Test Average Reward: -158, Test Average Length: 1000\n",
      "End of epoch: 4, Training Average Reward: -222, Training Average Length: 1000\n",
      "End of epoch: 4, Test Average Reward: -150, Test Average Length: 1000\n",
      "End of epoch: 5, Training Average Reward: -137, Training Average Length: 1000\n",
      "End of epoch: 5, Test Average Reward: -170, Test Average Length: 1000\n"
     ]
    }
   ],
   "source": [
    "env_name = 'Pendulum-v0'\n",
    "agent = sac(lambda : make_env(env_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let us record a video of trained agent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_animation(env, agent, save_dir):\n",
    "    \n",
    "    try:\n",
    "        env = gym.wrappers.Monitor(\n",
    "            env, save_dir, video_callable=lambda id: True, force=True, mode='evaluation')\n",
    "    except gym.error.Error as e:\n",
    "        print(e)\n",
    "\n",
    "    if not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "        \n",
    "    state, done, ep_ret, ep_len = env.reset(), False, 0, 0\n",
    "    while not done:\n",
    "        # Take deterministic actions at test time\n",
    "        state, reward, done, _ = env.step(agent.get_action(state, True))\n",
    "        ep_ret += reward\n",
    "        ep_len += 1\n",
    "\n",
    "    print('Reward: {}'.format(ep_ret))\n",
    "    env.close()\n",
    "            \n",
    "def display_animation(filepath):\n",
    "    video = io.open(filepath, 'r+b').read()\n",
    "    encoded = base64.b64encode(video)\n",
    "    return HTML(data='''<video alt=\"test\" controls>\n",
    "                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
    "                 </video>'''.format(encoded.decode('ascii')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reward: -230.3182719713348\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" controls>\n",
       "                <source 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type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Animate learned policy\n",
    "save_dir='./videos/tensorflow/sac/pendulum'\n",
    "env = gym.make(env_name)\n",
    "generate_animation(env, agent, save_dir=save_dir)\n",
    "[filepath] = glob.glob(os.path.join(save_dir, '*.mp4'))\n",
    "display_animation(filepath)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train and evaluate performance on LunarLander Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of epoch: 1, Training Average Reward: -206, Training Average Length: 111\n",
      "End of epoch: 1, Test Average Reward: -39, Test Average Length: 681\n",
      "End of epoch: 2, Training Average Reward: -201, Training Average Length: 109\n",
      "End of epoch: 2, Test Average Reward: -80, Test Average Length: 509\n",
      "End of epoch: 3, Training Average Reward: -214, Training Average Length: 167\n",
      "End of epoch: 3, Test Average Reward: -23, Test Average Length: 379\n",
      "End of epoch: 4, Training Average Reward: -24, Training Average Length: 325\n",
      "End of epoch: 4, Test Average Reward: -15, Test Average Length: 398\n",
      "End of epoch: 5, Training Average Reward: -74, Training Average Length: 223\n",
      "End of epoch: 5, Test Average Reward: -78, Test Average Length: 213\n",
      "Reward: -81.39071991737211\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<video alt=\"test\" controls>\n",
       "                <source 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type=\"video/mp4\" />\n",
       "                 </video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "env_name = 'LunarLanderContinuous-v2'\n",
    "agent1 = sac(lambda : make_env(env_name))\n",
    "\n",
    "# Animate learned policy\n",
    "save_dir='./videos/tensorflow/sac/lunar'\n",
    "env = gym.make(env_name)\n",
    "generate_animation(env, agent1, save_dir=save_dir)\n",
    "[filepath] = glob.glob(os.path.join(save_dir, '*.mp4'))\n",
    "display_animation(filepath)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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